Let’s face it, it’s the new year and many of us might have made those new year resolutions in January and are wondering how to fuel the way forward now that we’ve passed the start of 2024. For those of you who have managed to lose some weight for yourselves – good on you, but for the rest who are still trying – know that all is not lost.
Here comes A.I to save the day!
Artificial Intelligence can revolutionize weight loss through personalized health optimization. Imagine an AI system that integrates real-time biometric data from wearables with deep learning algorithms. This system would analyze everything: your heart rate, sleep patterns, stress levels, and even blood markers. Based on this data, it would construct a dynamically evolving, tailor-made regimen for diet, exercise, and sleep – just for you.
But it doesn't stop there.
By harnessing natural language processing (NLP), this AI could act as a 24/7 personal coach. It could provide real-time feedback during workouts, recommend meals when you're dining out, and even gently nudge you when it detects emotional eating triggers. If you’re in the grocery store, it could guide your choices, pushing you towards nutritious options that align with your current health metrics.
Generative AI is a technology that can create an impact
Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio, and synthetic. The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics, and videos in a matter of seconds. It has the potential to revolutionize many industries and aspects of our lives, including healthcare. Gen AI for weight loss specifically can be implemented using a variety of machine learning techniques, such as GANs, VAEs, and LLMs just to name a few.
Deep learning for personalized coaching and support
Deep learning models as a subset of A.I are a type of machine learning model that can learn complex patterns from large amounts of data. Deep learning models are often trained on artificial neural networks, which are inspired by the human brain.
Artificial neural networks are made up of interconnected nodes, which can process and transmit information. The nodes in an artificial neural network are arranged in layers, and each layer learns to perform a specific task. For example, the first layer of an artificial neural network might learn to identify the basic features of an image, such as lines, curves, and edges. The second layer might learn to combine these basic features to identify more complex shapes, such as faces and objects.
Deep learning models are trained on large amounts of data, such as diet and exercise data from people who have successfully lost weight. Once trained, these models can be used to identify patterns in a user’s data that may be contributing to their weight gain. For example, the model might identify that the user is consuming too many calories from carbohydrates or that they are not getting enough exercise.
Deep learning models can also be used to predict how a user’s body will respond to different weight loss strategies. This information can then be used to develop a personalized plan that is more likely to be successful for the user. For example, the model might predict that the user is more likely to lose weight if they follow a low-carb diet or if they exercise for 30 minutes per day, five days per week.
Finally, deep learning models can be used to provide personalized support and motivation to help users stay on track with their weight loss goals. This can be done by identifying the types of support and motivation that are most effective for different people. For example, the model might identify that some users prefer to have someone to hold them accountable, while others prefer to work independently.
This may be great for this application but for motivation and actually getting to know the user, the most suitable technology is Reinforcement Learning.
Reinforcement learning for real-time feedback and motivation
The whole concept is that it allows agents to learn how to behave in an environment by trial and error. Agents are rewarded for taking actions that lead to desired outcomes and penalized for taking actions that lead to undesired outcomes.
Reinforcement learning works by iteratively updating the agent’s policy. At each iteration, the agent takes an action in the current state, observes the next state and the reward received, and then updates its policy based on this new information.
Noom for example, is a popular weight loss app that uses artificial intelligence (AI) to help users lose weight and keep it off. The app provides users with personalized meal plans, exercise tracking, and support from a human coach. However one of the key ways that Noom uses AI to motivate users is through statistics.
Noom tracks a variety of data about its users, including their weight, food intake, exercise activity, and sleep patterns. This data is then used to generate personalized statistics and insights that can help users stay motivated. For example, Noom might show users a graph of their weight loss progress over time. This can help users see how far they have come and stay on track with their goals. Noom might also show users how their food choices and exercise habits are impacting their weight loss. This can help users identify areas where they need to make changes.
The Importance of Consistent Tracking & Success in Weight Loss
In addition to personalized statistics, Noom also provides users with access to a variety of general weight loss statistics. For example, Noom might show users a statistic that says that users who log their food consistently lose more weight than those who don’t. This can help users understand the importance of tracking their progress. Noom also uses AI to provide users with motivational messages and feedback.
Another Example of Generative A.I and weight loss Assistance
“Found” a new medically-assisted weight loss program launches Found Assistant, a generative A.I guide within the Found app that offers members the ability to ask questions and discover instant and relevant information on nutrition, movement, health habits, and more. Found Assistant is available 24/7 to provide personalized guidance throughout members’ journeys and further support them in reaching their weight and health goals.
Difficulty creating and keeping new routines, especially when it relates to weight care, happens for various reasons: some find it challenging to maintain a balanced diet while on the go, while others may need further guidance to fit movement into their daily schedules. Within such applications, members can leverage an open-type field to prompt the Found Assistant with specific questions or use Preset buttons to populate prompts for recipes, workout routines, meal plans, and habit-building.
In Conclusion
In all, Generaltive A.I applications for weight loss sees its effectiveness here isn't just on the personalization, but the adaptability. The AI adjusts its recommendations as it learns more about you, essentially evolving in real-time to your body's responses. It’s all about creating a seamless, intuitive experience that removes the burden of planning, decision-making, and self-monitoring from the individual, making weight loss more achievable than ever.
So go on, why not try a Generative A.I Application for weight loss this year – it might just work wonders for you.
